Hospital Readmission Risk: Predicting and Reducing Readmissions with KumoRFM скачать в хорошем качестве

Hospital Readmission Risk: Predicting and Reducing Readmissions with KumoRFM

Hackathon Spotlight: Predicting Hospital Readmission with KumoRFM Built by Kevin Soderholm, this prototype app tackles one of healthcare’s most costly and stressful challenges: early hospital readmission. It predicts which patients are most at risk, explains contributing factors, and even generates empathetic AI-avatar video messages for patients and clinicians. What it does • Predicts patient readmission risk within a user-selected window (1–30 days) • Identifies and ranks key factors driving that risk • Forecasts the most likely readmission diagnosis when risk is high • Creates patient-friendly video explanations with OpenAI + HeyGen • Automates next steps such as follow-up scheduling or case re-evaluation based on risk level How KumoRFM powers it • Links multi-table clinical data (patients, admissions, labs, meds, diagnoses) into a single relational graph in minutes • Runs real-time predictive queries (PQL) for dynamic time windows, no retraining • Eliminates heavy feature engineering and complex model pipelines • No training. No feature engineering. Instant predictions directly on live, structured healthcare data. KumoRFM is free to use – start here: https://kumorfm.ai/ Huge congratulations to Kevin for building a powerful healthcare prototype that can reduce costs, improve outcomes, and bring clear, human-centered AI to hospitals everywhere.